Smart antennas form an integral component in realizing the high spectral efficiency requirements of next generation wireless standards. Adaptive beamforming is a popular strategy that helps leverage the smart antenna benefits in environments rich in multipath scattering. Smart antennas consist of an array of omni-directional elements, with the bulk of sophistication residing in signal processing. Smart antennas represent a sophisticated physical layer technology that is capable of providing higher spectral efficiencies. Leveraging smart antenna gains can be accomplished through several strategies namely, spatial multiplexing, space-time codes and beamforming. While the former two are primarily associated with MIMO (multiple-input multiple-output), wherein both ends of the link possess smart antennas, beamforming is possible even if only one end of the link is smart antenna enabled. Beamforming employs multiple antenna elements to focus the radiated signal energy in specific directions towards the receiver, thereby increasing its SINR. Beamforming can vary from simple switched type to fully adaptive type. While switched beamforming is normally implemented as an open-loop solution and provides benefits in line of sight environments, an adaptive beamforming system implemented as a closed-loop solution (where channel feedback is required from the receivers) is required to effectively counteract multipath, which is prevalent indoors. Realization of the (theoretical) benefits of adaptive beamforming has led to its adoption in several next-generation wireless standards oriented both outdoors such as LTE and WiMAX, and recent indoor WPAN standards such as WirelessHD and NGmS which utilize the 60 Ghz spectrum.
Adaptive beamforming involves three main steps. The first step is channel sounding where the transmitter (Tx) sends a pilot packet on the channel. The second step is channel estimation and feedback where the receiver (Rx) estimates the channel gain and feeds this information back to the transmitter. The third step is beam computation where the transmitter adapts the beam pattern based on the channel feedback from the receiver. The performance of adaptive beamforming is closely tied to the accuracy of estimating the complex channel coefficients (which characterize the channel gain) between the Tx and Rx. Inaccurate estimates could potentially degrade performance to worse than an omni system. An oscillator drift between the Tx and Rx introduces a phase and frequency offset, thereby corrupting the channel phase component of the estimated samples. In addition, variation of the oscillator-induced phase offset with time makes it harder to isolate the channel phase component.
In the case of beamforming antennas, the signals that are transmitted to each of these antenna elements can be weighted in both amplitude and phase to produce a desired beam pattern that increases the SNR at the receiver, resulting in an array gain. The capacity of a beamformed link between a K element beamforming Tx and an omni Rx is given by,C=log(1+Kρ)  (1)where the array gain is Kρ, ρ being the received SNR due to an omni Tx. This represents the asymptotic capacity gain achievable in free space environments. However, indoor wireless channels are impaired by random fluctuations in signal level along the space and time dimensions referred to as fading due to which the above array gain is not achievable in practice. To mitigate the effect of fading, multiple transmit elements together with appropriate signal processing can be used to enhance and stabilize the signal level at the receiver. The multiple and ideally independent observations of the signal ensure that the link reliability or error rate performance at the receiver is improved since the probability of all of them being in a fade at the same time reduces sharply with the number of observations. This gain in SNR for a required probability of error is called the diversity gain.
The weights used to modify the amplitude and phase of the signals at the Tx antenna array can be written asw=[w1w2 . . . wK]  (2)
When a transmitter with multiple elements communicates with a receiver which has an omni-directional antenna, the wireless channel so formed is called a Multiple Input Single Output (MISO) channel. The baseband channel model for a MISO channel with beamforming is given byy=hTx+z  (3)where the column vector h=[h1 h2 . . . hK]T denotes the channel, x is the K×1 vector of the transmitted signals, y is the received signal and z is the additive White Gaussian noise. A beamformer is defined as a weight vector w which translates each transmit symbol s to the signal vector x=ws to be transmitted from the K antennas.
Beamforming is a technique, where the weights w are adapted to get a desired beam pattern, so that the SNR at the receiver is maximized. Depending on the level of sophistication in adapting weights, there are two main types of beamforming namely, switched and adaptive. In the case of switched beamforming, a set of pre-determined beam patterns covering the entire azimuth of 360 degrees are made available. Each of these beam patterns has a main lobe of maximum gain and some side lobes representing leakage of energy. The patterns are generated by selecting weights that vary the phase across the antenna elements, while keeping the amplitude the same; the latter is achieved by splitting the transmit power equally across all the elements. As switched beamforming is normally implemented as an open-loop procedure without channel feedback from the Rx, a Tx will tend to choose a pattern that is in the physical direction of the Rx expecting that beam to yield the strongest signal strength at the Rx. In the presence of multipath, a beam pointing in the physical direction of the Rx may no longer yield the strongest signal at the Rx and almost never will in a NLOS (non-line of sight) environment.
In adaptive beamforming, real-time channel feedback from the client is employed to adapt the beam pattern at the Tx. Beams are no longer selected to point in the direction of the Rx, but instead is adapted in the signal domain to maximize the resulting SINR at the client. The resulting beam pattern may not have the single main lobe structure of a switched beam as shown in FIG. 1 but is optimized to reinforce the multipath components of the signals arriving from the different Tx antenna elements, at the Rx. While the need for channel estimation and feedback makes its implementation complex, it has the potential to counteract multipath effects, which are dominant indoors. Given a channel gain vector h, the transmit array's weights are determined to be its complex conjugate, so that the phase difference between the signal components arriving at the Rx transmitted through the different antenna elements are corrected, yielding high SINR due to coherent combining at the Rx. With reference to equation 3, this is achieved by choosing
      w    i    =                    h        i            *                                                          ∑                              i                =                1                            K                        ⁢                                                  ⁢                                        ⁢                              h                i                                                              2            given the channel [h1 h2 . . . hK] to a single receiver. An illustration of such an adaptive pattern is shown in FIG. 1. This is a pattern that was seen to be the optimal beam pattern in an indoor (multipath-rich) office environment and was obtained using actual experiments. It is clear that the adaptive pattern is significantly different from the corresponding switched beam pattern (resulting in a 5 dB improvement in SNR at the receiver in this case).
As pointed out in Equation 1, increasing the beamforming elements (referred to as degrees of freedom—dof) at the AP will only increase the array gain linearly, contributing only to a logarithmic increase in the data rate at the client, assuming SU beamforming is used. However, by accommodating and jointly beamforming to multiple clients, the dof can be more efficiently utilized resulting in a much better scaling of capacity with MU beamforming. Further, in MU beamforming, the AP can produce beam patterns which improve the signal strength at the clients to which it is beaming data and at the same time suppress interference to the clients that are communicating with other APs.